code: def check_experts_accuracy(exml_benchmark):
      """Sanity check. Compute experts accuracy on the train stream."""
      print(
          type(exml_benchmark).__name__,
          "testing expert models on the original train stream",
      )
      for i, exp in enumerate(exml_benchmark.expert_models_stream):
          model = exp.expert_model
          model.to("cuda")
          acc = TaskAwareAccuracy()
  
          train_data = exml_benchmark.original_benchmark.train_stream[
              i
          ].dataset
          for x, y, t in DataLoader(
              train_data, batch_size=256, pin_memory=True, num_workers=4
          ):
              x, y, t = x.to("cuda"), y.to("cuda"), t.to("cuda")
              y_pred = model(x)
              acc.update(y_pred, y, t)
          print(f"(i={i}) Original model accuracy: {acc.result()}")
          model.to("cpu")
  
imports: import urllib
  import os
  import torch
  from torch import nn
  from torch.utils.data import DataLoader
  from torchvision.models import mobilenet_v2
  from torchvision.transforms import RandomHorizontalFlip, RandomCrop, RandomRotation, ToTensor, CenterCrop, Normalize, Resize
  from avalanche.benchmarks.classic.ccifar10 import SplitCIFAR10
  from avalanche.benchmarks.classic.cmnist import SplitMNIST
  from avalanche.benchmarks.classic.core50 import CORe50
  from avalanche.benchmarks.datasets.dataset_utils import default_dataset_location
  from avalanche.benchmarks.utils.utils import concat_datasets
  from avalanche.models import LeNet5, SlimResNet18
  from torchvision.transforms import Compose
  from avalanche.evaluation.metrics import TaskAwareAccuracy
  from avalanche.benchmarks import ExModelCLScenario, nc_benchmark
  import copy
  import urllib.request
label_desc: Use image node_id 2 for calling a global function with example usage: check_experts_accuracy(exml_benchmark) without return types